Application of Principal Component Analysis (PCA) to Reduce Multicollinearity Exchange Rate Currency of Some Countries in Asia Period 2004-2014
International Journal of Educational Methodology Volume 3, Issue 2, 75 - 83. ISSN: 2469-9632 http://www.ijem.com/ Application of Principal Component Analysis (PCA) to Reduce Multicollinearity Exchange Rate Currency of Some Countries in Asia Period 2004-2014 Sri Rahayu TeguhSugiarto* Diponegoro University, INDONESIA Brawijaya University, INDONESIA LudiroMadu Holiawati Ahmad Subagyo UPN ’Veteran’ Yogyakarta, INDONESIA UniversitasPamulang, INDONESIA GICI Business School, INDONESIA Abstract:This study aims to apply the model Principal component Analysis to reduce multicollinearity on variable currency exchange rate in eight countries in Asia against US Dollar including the Yen (Japan), Won (South Korea), Dollar (Hongkong), Yuan (China), Bath (Thailand), Rupiah (Indonesia), Ringgit (Malaysia), Dollar (Singapore). It looks at yield levels of multicolinierity which is smaller in comparison with PCA applications using multiple regression. This study used multiple regression test and PCA application to investigate the differences in multicollinearity at yield. From this research, it can be concluded that the use of PCA analysis applications can reduce multicollinearity in variables in doing research. Keywords:Principal component analysis (PCA), multiple regression, matrix var-cov, exchange rate. To cite this article: Rahayu, S.,Sugiarto, T., Madu, L., Holiawati, &Subagyo,A. (2017). Application of principal component analysis (PCA) to reduce multicollinearity exchange rate currency of some countries in asia period 2004-2014. International Journal of Educational Methodology, 3(2), 75-83.doi: 10.12973/ijem.3.2.75 Introduction In Indonesia's economy in particular, Bank of Indonesia (BI) predicts the exchange rate is in the range of Rp 13,500 to Rp 13,800. However, when seen as a point averages, the central bank predicts the rupiah will be at Rp 13,600 per US dollar by the end of 2016.
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